Overview

Brought to you by YData

Dataset statistics

Number of variables15
Number of observations428
Missing cells2
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory50.3 KiB
Average record size in memory120.3 B

Variable types

Categorical4
Text3
Numeric8

Alerts

Cylinders is highly overall correlated with EngineSize and 6 other fieldsHigh correlation
DriveTrain is highly overall correlated with MakeHigh correlation
EngineSize is highly overall correlated with Cylinders and 6 other fieldsHigh correlation
Horsepower is highly overall correlated with Cylinders and 5 other fieldsHigh correlation
Length is highly overall correlated with Cylinders and 4 other fieldsHigh correlation
MPG_City is highly overall correlated with Cylinders and 7 other fieldsHigh correlation
MPG_Highway is highly overall correlated with Cylinders and 6 other fieldsHigh correlation
Make is highly overall correlated with DriveTrain and 1 other fieldsHigh correlation
Origin is highly overall correlated with MakeHigh correlation
Type is highly overall correlated with MPG_City and 1 other fieldsHigh correlation
Weight is highly overall correlated with Cylinders and 6 other fieldsHigh correlation
Wheelbase is highly overall correlated with Cylinders and 6 other fieldsHigh correlation

Reproduction

Analysis started2025-08-13 10:02:16.492688
Analysis finished2025-08-13 10:02:18.448256
Duration1.96 second
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Make
Categorical

High correlation 

Distinct38
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Toyota
 
28
Chevrolet
 
27
Mercedes-Benz
 
26
Ford
 
23
BMW
 
20
Other values (33)
304 

Length

Max length13
Median length9
Mean length6.4672897
Min length3

Characters and Unicode

Total characters2768
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.2%

Sample

1st rowAcura
2nd rowAcura
3rd rowAcura
4th rowAcura
5th rowAcura

Common Values

ValueCountFrequency (%)
Toyota 28
 
6.5%
Chevrolet 27
 
6.3%
Mercedes-Benz 26
 
6.1%
Ford 23
 
5.4%
BMW 20
 
4.7%
Audi 19
 
4.4%
Honda 17
 
4.0%
Nissan 17
 
4.0%
Volkswagen 15
 
3.5%
Chrysler 15
 
3.5%
Other values (28) 221
51.6%

Length

2025-08-13T15:32:18.467126image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
toyota 28
 
6.5%
chevrolet 27
 
6.3%
mercedes-benz 26
 
6.0%
ford 23
 
5.3%
bmw 20
 
4.6%
audi 19
 
4.4%
honda 17
 
3.9%
nissan 17
 
3.9%
chrysler 15
 
3.5%
volkswagen 15
 
3.5%
Other values (29) 224
52.0%

Most occurring characters

ValueCountFrequency (%)
e 241
 
8.7%
a 216
 
7.8%
o 210
 
7.6%
r 173
 
6.2%
i 172
 
6.2%
n 145
 
5.2%
u 143
 
5.2%
s 139
 
5.0%
d 135
 
4.9%
l 100
 
3.6%
Other values (36) 1094
39.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 241
 
8.7%
a 216
 
7.8%
o 210
 
7.6%
r 173
 
6.2%
i 172
 
6.2%
n 145
 
5.2%
u 143
 
5.2%
s 139
 
5.0%
d 135
 
4.9%
l 100
 
3.6%
Other values (36) 1094
39.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 241
 
8.7%
a 216
 
7.8%
o 210
 
7.6%
r 173
 
6.2%
i 172
 
6.2%
n 145
 
5.2%
u 143
 
5.2%
s 139
 
5.0%
d 135
 
4.9%
l 100
 
3.6%
Other values (36) 1094
39.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 241
 
8.7%
a 216
 
7.8%
o 210
 
7.6%
r 173
 
6.2%
i 172
 
6.2%
n 145
 
5.2%
u 143
 
5.2%
s 139
 
5.0%
d 135
 
4.9%
l 100
 
3.6%
Other values (36) 1094
39.5%

Model
Text

Distinct425
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2025-08-13T15:32:18.601889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length39
Median length30
Mean length14.514019
Min length2

Characters and Unicode

Total characters6212
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique422 ?
Unique (%)98.6%

Sample

1st rowMDX
2nd rowRSX Type S 2dr
3rd rowTSX 4dr
4th rowTL 4dr
5th row3.5 RL 4dr
ValueCountFrequency (%)
4dr 197
 
15.7%
2dr 94
 
7.5%
convertible 41
 
3.3%
lx 22
 
1.8%
ls 21
 
1.7%
v6 19
 
1.5%
se 17
 
1.4%
coupe 15
 
1.2%
cab 15
 
1.2%
s 14
 
1.1%
Other values (421) 800
63.7%
2025-08-13T15:32:18.776086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
827
 
13.3%
r 577
 
9.3%
d 357
 
5.7%
a 327
 
5.3%
e 326
 
5.2%
4 237
 
3.8%
o 228
 
3.7%
t 227
 
3.7%
S 209
 
3.4%
i 205
 
3.3%
Other values (58) 2692
43.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6212
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
827
 
13.3%
r 577
 
9.3%
d 357
 
5.7%
a 327
 
5.3%
e 326
 
5.2%
4 237
 
3.8%
o 228
 
3.7%
t 227
 
3.7%
S 209
 
3.4%
i 205
 
3.3%
Other values (58) 2692
43.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6212
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
827
 
13.3%
r 577
 
9.3%
d 357
 
5.7%
a 327
 
5.3%
e 326
 
5.2%
4 237
 
3.8%
o 228
 
3.7%
t 227
 
3.7%
S 209
 
3.4%
i 205
 
3.3%
Other values (58) 2692
43.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6212
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
827
 
13.3%
r 577
 
9.3%
d 357
 
5.7%
a 327
 
5.3%
e 326
 
5.2%
4 237
 
3.8%
o 228
 
3.7%
t 227
 
3.7%
S 209
 
3.4%
i 205
 
3.3%
Other values (58) 2692
43.3%

Type
Categorical

High correlation 

Distinct6
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Sedan
262 
SUV
60 
Sports
49 
Wagon
30 
Truck
 
24

Length

Max length6
Median length5
Mean length4.8411215
Min length3

Characters and Unicode

Total characters2072
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSUV
2nd rowSedan
3rd rowSedan
4th rowSedan
5th rowSedan

Common Values

ValueCountFrequency (%)
Sedan 262
61.2%
SUV 60
 
14.0%
Sports 49
 
11.4%
Wagon 30
 
7.0%
Truck 24
 
5.6%
Hybrid 3
 
0.7%

Length

2025-08-13T15:32:18.810388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-13T15:32:18.832593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
sedan 262
61.2%
suv 60
 
14.0%
sports 49
 
11.4%
wagon 30
 
7.0%
truck 24
 
5.6%
hybrid 3
 
0.7%

Most occurring characters

ValueCountFrequency (%)
S 371
17.9%
a 292
14.1%
n 292
14.1%
d 265
12.8%
e 262
12.6%
o 79
 
3.8%
r 76
 
3.7%
U 60
 
2.9%
V 60
 
2.9%
t 49
 
2.4%
Other values (12) 266
12.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2072
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 371
17.9%
a 292
14.1%
n 292
14.1%
d 265
12.8%
e 262
12.6%
o 79
 
3.8%
r 76
 
3.7%
U 60
 
2.9%
V 60
 
2.9%
t 49
 
2.4%
Other values (12) 266
12.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2072
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 371
17.9%
a 292
14.1%
n 292
14.1%
d 265
12.8%
e 262
12.6%
o 79
 
3.8%
r 76
 
3.7%
U 60
 
2.9%
V 60
 
2.9%
t 49
 
2.4%
Other values (12) 266
12.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2072
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 371
17.9%
a 292
14.1%
n 292
14.1%
d 265
12.8%
e 262
12.6%
o 79
 
3.8%
r 76
 
3.7%
U 60
 
2.9%
V 60
 
2.9%
t 49
 
2.4%
Other values (12) 266
12.8%

Origin
Categorical

High correlation 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Asia
158 
USA
147 
Europe
123 

Length

Max length6
Median length4
Mean length4.2313084
Min length3

Characters and Unicode

Total characters1811
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsia
2nd rowAsia
3rd rowAsia
4th rowAsia
5th rowAsia

Common Values

ValueCountFrequency (%)
Asia 158
36.9%
USA 147
34.3%
Europe 123
28.7%

Length

2025-08-13T15:32:18.863513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-13T15:32:18.882252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
asia 158
36.9%
usa 147
34.3%
europe 123
28.7%

Most occurring characters

ValueCountFrequency (%)
A 305
16.8%
s 158
8.7%
i 158
8.7%
a 158
8.7%
U 147
8.1%
S 147
8.1%
E 123
6.8%
u 123
6.8%
r 123
6.8%
o 123
6.8%
Other values (2) 246
13.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1811
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A 305
16.8%
s 158
8.7%
i 158
8.7%
a 158
8.7%
U 147
8.1%
S 147
8.1%
E 123
6.8%
u 123
6.8%
r 123
6.8%
o 123
6.8%
Other values (2) 246
13.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1811
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A 305
16.8%
s 158
8.7%
i 158
8.7%
a 158
8.7%
U 147
8.1%
S 147
8.1%
E 123
6.8%
u 123
6.8%
r 123
6.8%
o 123
6.8%
Other values (2) 246
13.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1811
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A 305
16.8%
s 158
8.7%
i 158
8.7%
a 158
8.7%
U 147
8.1%
S 147
8.1%
E 123
6.8%
u 123
6.8%
r 123
6.8%
o 123
6.8%
Other values (2) 246
13.6%

DriveTrain
Categorical

High correlation 

Distinct3
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
Front
226 
Rear
110 
All
92 

Length

Max length5
Median length5
Mean length4.3130841
Min length3

Characters and Unicode

Total characters1846
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll
2nd rowFront
3rd rowFront
4th rowFront
5th rowFront

Common Values

ValueCountFrequency (%)
Front 226
52.8%
Rear 110
25.7%
All 92
21.5%

Length

2025-08-13T15:32:18.907804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-08-13T15:32:18.926739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
front 226
52.8%
rear 110
25.7%
all 92
21.5%

Most occurring characters

ValueCountFrequency (%)
r 336
18.2%
F 226
12.2%
o 226
12.2%
n 226
12.2%
t 226
12.2%
l 184
10.0%
R 110
 
6.0%
e 110
 
6.0%
a 110
 
6.0%
A 92
 
5.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1846
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 336
18.2%
F 226
12.2%
o 226
12.2%
n 226
12.2%
t 226
12.2%
l 184
10.0%
R 110
 
6.0%
e 110
 
6.0%
a 110
 
6.0%
A 92
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1846
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 336
18.2%
F 226
12.2%
o 226
12.2%
n 226
12.2%
t 226
12.2%
l 184
10.0%
R 110
 
6.0%
e 110
 
6.0%
a 110
 
6.0%
A 92
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1846
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 336
18.2%
F 226
12.2%
o 226
12.2%
n 226
12.2%
t 226
12.2%
l 184
10.0%
R 110
 
6.0%
e 110
 
6.0%
a 110
 
6.0%
A 92
 
5.0%

MSRP
Text

Distinct410
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2025-08-13T15:32:19.065045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.0093458
Min length7

Characters and Unicode

Total characters3000
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique392 ?
Unique (%)91.6%

Sample

1st row$36,945
2nd row$23,820
3rd row$26,990
4th row$33,195
5th row$43,755
ValueCountFrequency (%)
33,995 2
 
0.5%
19,635 2
 
0.5%
21,595 2
 
0.5%
19,860 2
 
0.5%
49,995 2
 
0.5%
31,545 2
 
0.5%
35,940 2
 
0.5%
27,490 2
 
0.5%
25,700 2
 
0.5%
34,495 2
 
0.5%
Other values (400) 408
95.3%
2025-08-13T15:32:19.237574image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
$ 428
14.3%
, 428
14.3%
5 334
11.1%
0 297
9.9%
2 281
9.4%
9 236
7.9%
3 218
7.3%
1 206
6.9%
4 200
6.7%
6 129
 
4.3%
Other values (2) 243
8.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
$ 428
14.3%
, 428
14.3%
5 334
11.1%
0 297
9.9%
2 281
9.4%
9 236
7.9%
3 218
7.3%
1 206
6.9%
4 200
6.7%
6 129
 
4.3%
Other values (2) 243
8.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
$ 428
14.3%
, 428
14.3%
5 334
11.1%
0 297
9.9%
2 281
9.4%
9 236
7.9%
3 218
7.3%
1 206
6.9%
4 200
6.7%
6 129
 
4.3%
Other values (2) 243
8.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
$ 428
14.3%
, 428
14.3%
5 334
11.1%
0 297
9.9%
2 281
9.4%
9 236
7.9%
3 218
7.3%
1 206
6.9%
4 200
6.7%
6 129
 
4.3%
Other values (2) 243
8.1%
Distinct425
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2025-08-13T15:32:19.375415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length7
Mean length7.0070093
Min length6

Characters and Unicode

Total characters2999
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique422 ?
Unique (%)98.6%

Sample

1st row$33,337
2nd row$21,761
3rd row$24,647
4th row$30,299
5th row$39,014
ValueCountFrequency (%)
19,638 2
 
0.5%
14,207 2
 
0.5%
68,306 2
 
0.5%
34,483 1
 
0.2%
29,566 1
 
0.2%
30,299 1
 
0.2%
39,014 1
 
0.2%
41,100 1
 
0.2%
79,978 1
 
0.2%
23,508 1
 
0.2%
Other values (415) 415
97.0%
2025-08-13T15:32:19.540202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
$ 428
14.3%
, 428
14.3%
2 300
10.0%
1 289
9.6%
3 251
8.4%
4 205
6.8%
8 194
6.5%
0 187
6.2%
5 186
6.2%
7 184
6.1%
Other values (2) 347
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2999
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
$ 428
14.3%
, 428
14.3%
2 300
10.0%
1 289
9.6%
3 251
8.4%
4 205
6.8%
8 194
6.5%
0 187
6.2%
5 186
6.2%
7 184
6.1%
Other values (2) 347
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2999
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
$ 428
14.3%
, 428
14.3%
2 300
10.0%
1 289
9.6%
3 251
8.4%
4 205
6.8%
8 194
6.5%
0 187
6.2%
5 186
6.2%
7 184
6.1%
Other values (2) 347
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2999
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
$ 428
14.3%
, 428
14.3%
2 300
10.0%
1 289
9.6%
3 251
8.4%
4 205
6.8%
8 194
6.5%
0 187
6.2%
5 186
6.2%
7 184
6.1%
Other values (2) 347
11.6%

EngineSize
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.196729
Minimum1.3
Maximum8.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2025-08-13T15:32:19.574578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.3
5-th percentile1.7
Q12.375
median3
Q33.9
95-th percentile5.3
Maximum8.3
Range7
Interquartile range (IQR)1.525

Descriptive statistics

Standard deviation1.1085947
Coefficient of variation (CV)0.34679034
Kurtosis0.54194354
Mean3.196729
Median Absolute Deviation (MAD)0.8
Skewness0.70815198
Sum1368.2
Variance1.2289822
MonotonicityNot monotonic
2025-08-13T15:32:19.608394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
3 42
 
9.8%
3.5 34
 
7.9%
2 30
 
7.0%
2.5 26
 
6.1%
2.4 23
 
5.4%
1.8 23
 
5.4%
4.6 21
 
4.9%
4.2 20
 
4.7%
3.2 18
 
4.2%
3.8 17
 
4.0%
Other values (33) 174
40.7%
ValueCountFrequency (%)
1.3 2
 
0.5%
1.4 1
 
0.2%
1.5 6
 
1.4%
1.6 10
 
2.3%
1.7 4
 
0.9%
1.8 23
5.4%
1.9 3
 
0.7%
2 30
7.0%
2.2 15
3.5%
2.3 13
3.0%
ValueCountFrequency (%)
8.3 1
 
0.2%
6.8 1
 
0.2%
6 6
1.4%
5.7 3
 
0.7%
5.6 2
 
0.5%
5.5 3
 
0.7%
5.4 2
 
0.5%
5.3 5
1.2%
5 8
1.9%
4.8 2
 
0.5%

Cylinders
Real number (ℝ)

High correlation 

Distinct7
Distinct (%)1.6%
Missing2
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean5.8075117
Minimum3
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2025-08-13T15:32:19.632921image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q14
median6
Q36
95-th percentile8
Maximum12
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5584426
Coefficient of variation (CV)0.26834946
Kurtosis0.44037832
Mean5.8075117
Median Absolute Deviation (MAD)2
Skewness0.5927852
Sum2474
Variance2.4287434
MonotonicityNot monotonic
2025-08-13T15:32:19.654908image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
6 190
44.4%
4 136
31.8%
8 87
20.3%
5 7
 
1.6%
12 3
 
0.7%
10 2
 
0.5%
3 1
 
0.2%
(Missing) 2
 
0.5%
ValueCountFrequency (%)
3 1
 
0.2%
4 136
31.8%
5 7
 
1.6%
6 190
44.4%
8 87
20.3%
10 2
 
0.5%
12 3
 
0.7%
ValueCountFrequency (%)
12 3
 
0.7%
10 2
 
0.5%
8 87
20.3%
6 190
44.4%
5 7
 
1.6%
4 136
31.8%
3 1
 
0.2%

Horsepower
Real number (ℝ)

High correlation 

Distinct110
Distinct (%)25.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215.88551
Minimum73
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2025-08-13T15:32:19.683664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum73
5-th percentile115
Q1165
median210
Q3255
95-th percentile338.25
Maximum500
Range427
Interquartile range (IQR)90

Descriptive statistics

Standard deviation71.836032
Coefficient of variation (CV)0.33275059
Kurtosis1.5521586
Mean215.88551
Median Absolute Deviation (MAD)45
Skewness0.93033074
Sum92399
Variance5160.4154
MonotonicityNot monotonic
2025-08-13T15:32:19.718135image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 17
 
4.0%
215 14
 
3.3%
210 14
 
3.3%
240 13
 
3.0%
225 13
 
3.0%
220 12
 
2.8%
140 12
 
2.8%
300 11
 
2.6%
170 11
 
2.6%
130 10
 
2.3%
Other values (100) 301
70.3%
ValueCountFrequency (%)
73 1
 
0.2%
93 1
 
0.2%
100 1
 
0.2%
103 5
1.2%
104 3
0.7%
108 5
1.2%
110 2
 
0.5%
115 6
1.4%
117 1
 
0.2%
119 2
 
0.5%
ValueCountFrequency (%)
500 1
 
0.2%
493 3
0.7%
477 1
 
0.2%
450 1
 
0.2%
420 1
 
0.2%
390 4
0.9%
350 2
 
0.5%
349 2
 
0.5%
345 1
 
0.2%
340 6
1.4%

MPG_City
Real number (ℝ)

High correlation 

Distinct28
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.060748
Minimum10
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2025-08-13T15:32:19.747715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14
Q117
median19
Q321.25
95-th percentile29
Maximum60
Range50
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation5.2382176
Coefficient of variation (CV)0.26111777
Kurtosis15.791147
Mean20.060748
Median Absolute Deviation (MAD)2
Skewness2.7820718
Sum8586
Variance27.438924
MonotonicityNot monotonic
2025-08-13T15:32:19.774679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
18 69
16.1%
20 57
13.3%
17 41
9.6%
21 38
8.9%
19 37
8.6%
16 31
7.2%
24 22
 
5.1%
26 22
 
5.1%
22 18
 
4.2%
15 17
 
4.0%
Other values (18) 76
17.8%
ValueCountFrequency (%)
10 2
 
0.5%
12 4
 
0.9%
13 12
 
2.8%
14 13
 
3.0%
15 17
 
4.0%
16 31
7.2%
17 41
9.6%
18 69
16.1%
19 37
8.6%
20 57
13.3%
ValueCountFrequency (%)
60 1
 
0.2%
59 1
 
0.2%
46 1
 
0.2%
38 1
 
0.2%
36 1
 
0.2%
35 2
 
0.5%
33 1
 
0.2%
32 7
1.6%
31 1
 
0.2%
29 7
1.6%

MPG_Highway
Real number (ℝ)

High correlation 

Distinct33
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.843458
Minimum12
Maximum66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2025-08-13T15:32:19.802353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile18
Q124
median26
Q329
95-th percentile36
Maximum66
Range54
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.7412007
Coefficient of variation (CV)0.21387709
Kurtosis6.0456107
Mean26.843458
Median Absolute Deviation (MAD)3
Skewness1.2523953
Sum11489
Variance32.961386
MonotonicityNot monotonic
2025-08-13T15:32:19.832171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
26 54
12.6%
25 44
 
10.3%
28 38
 
8.9%
29 34
 
7.9%
27 28
 
6.5%
24 25
 
5.8%
30 24
 
5.6%
23 16
 
3.7%
21 16
 
3.7%
19 16
 
3.7%
Other values (23) 133
31.1%
ValueCountFrequency (%)
12 1
 
0.2%
13 1
 
0.2%
14 1
 
0.2%
16 2
 
0.5%
17 9
2.1%
18 11
2.6%
19 16
3.7%
20 13
3.0%
21 16
3.7%
22 13
3.0%
ValueCountFrequency (%)
66 1
 
0.2%
51 2
 
0.5%
46 1
 
0.2%
44 1
 
0.2%
43 2
 
0.5%
40 3
0.7%
39 1
 
0.2%
38 3
0.7%
37 5
1.2%
36 5
1.2%

Weight
Real number (ℝ)

High correlation 

Distinct348
Distinct (%)81.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3577.9533
Minimum1850
Maximum7190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2025-08-13T15:32:19.864719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1850
5-th percentile2513
Q13104
median3474.5
Q33977.75
95-th percentile4995.45
Maximum7190
Range5340
Interquartile range (IQR)873.75

Descriptive statistics

Standard deviation758.98321
Coefficient of variation (CV)0.21212776
Kurtosis1.6887885
Mean3577.9533
Median Absolute Deviation (MAD)428
Skewness0.89182423
Sum1531364
Variance576055.52
MonotonicityNot monotonic
2025-08-13T15:32:19.900680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3175 4
 
0.9%
3450 4
 
0.9%
3285 4
 
0.9%
3428 3
 
0.7%
3197 3
 
0.7%
4052 3
 
0.7%
3803 3
 
0.7%
3351 3
 
0.7%
3217 3
 
0.7%
2524 3
 
0.7%
Other values (338) 395
92.3%
ValueCountFrequency (%)
1850 1
0.2%
2035 1
0.2%
2055 1
0.2%
2085 1
0.2%
2195 1
0.2%
2255 1
0.2%
2290 1
0.2%
2339 1
0.2%
2340 1
0.2%
2348 1
0.2%
ValueCountFrequency (%)
7190 1
0.2%
6400 1
0.2%
6133 1
0.2%
5969 1
0.2%
5879 1
0.2%
5678 1
0.2%
5590 1
0.2%
5464 1
0.2%
5440 1
0.2%
5423 1
0.2%

Wheelbase
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.15421
Minimum89
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2025-08-13T15:32:19.931741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum89
5-th percentile95.35
Q1103
median107
Q3112
95-th percentile123
Maximum144
Range55
Interquartile range (IQR)9

Descriptive statistics

Standard deviation8.311813
Coefficient of variation (CV)0.076851501
Kurtosis2.1336492
Mean108.15421
Median Absolute Deviation (MAD)5
Skewness0.96228697
Sum46290
Variance69.086235
MonotonicityNot monotonic
2025-08-13T15:32:19.962749image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
107 45
 
10.5%
103 30
 
7.0%
106 27
 
6.3%
112 25
 
5.8%
104 24
 
5.6%
105 21
 
4.9%
115 20
 
4.7%
111 17
 
4.0%
109 17
 
4.0%
101 16
 
3.7%
Other values (30) 186
43.5%
ValueCountFrequency (%)
89 2
 
0.5%
93 9
2.1%
95 11
2.6%
96 5
 
1.2%
97 3
 
0.7%
98 11
2.6%
99 11
2.6%
100 7
1.6%
101 16
3.7%
102 16
3.7%
ValueCountFrequency (%)
144 2
0.5%
140 1
 
0.2%
137 1
 
0.2%
133 2
0.5%
131 1
 
0.2%
130 4
0.9%
129 2
0.5%
128 2
0.5%
126 2
0.5%
124 3
0.7%

Length
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186.36215
Minimum143
Maximum238
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.5 KiB
2025-08-13T15:32:19.995934image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum143
5-th percentile163
Q1178
median187
Q3194
95-th percentile212
Maximum238
Range95
Interquartile range (IQR)16

Descriptive statistics

Standard deviation14.357991
Coefficient of variation (CV)0.077043495
Kurtosis0.61472451
Mean186.36215
Median Absolute Deviation (MAD)9
Skewness0.18197703
Sum79763
Variance206.15191
MonotonicityNot monotonic
2025-08-13T15:32:20.029743image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178 27
 
6.3%
190 22
 
5.1%
187 17
 
4.0%
192 16
 
3.7%
188 15
 
3.5%
179 14
 
3.3%
191 14
 
3.3%
177 13
 
3.0%
200 13
 
3.0%
183 12
 
2.8%
Other values (57) 265
61.9%
ValueCountFrequency (%)
143 1
 
0.2%
144 1
 
0.2%
150 1
 
0.2%
153 2
0.5%
154 1
 
0.2%
155 2
0.5%
156 2
0.5%
158 2
0.5%
159 3
0.7%
160 1
 
0.2%
ValueCountFrequency (%)
238 1
 
0.2%
230 1
 
0.2%
227 1
 
0.2%
224 1
 
0.2%
222 2
 
0.5%
221 2
 
0.5%
219 3
0.7%
218 3
0.7%
215 2
 
0.5%
212 7
1.6%

Interactions

2025-08-13T15:32:18.177918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:16.664232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.024031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.208494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.395141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.586006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.778243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.979866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:18.202426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:16.716168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.047437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.232771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.419616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.613437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.803015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:18.005431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:18.226196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:16.788535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.068360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.255869image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.442972image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.636382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.826568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:18.028147image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:18.249226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:16.853536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.093113image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.277507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.465629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.660204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.851652image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:18.052766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:18.273764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:16.902273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.116314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.300351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.489280image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.683176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.876840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:18.081325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:18.297097image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:16.948726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.138530image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.324107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.512582image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.706989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.906678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:18.106920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:18.320760image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:16.973735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.162190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.347886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.536849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.730619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.931503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:18.130846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:18.348973image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:16.999190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.185114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.372038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.560513image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.754205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:17.956107image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-08-13T15:32:18.153948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-08-13T15:32:20.056939image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CylindersDriveTrainEngineSizeHorsepowerLengthMPG_CityMPG_HighwayMakeOriginTypeWeightWheelbase
Cylinders1.0000.3100.9240.8150.566-0.834-0.7390.3690.2590.2740.7840.610
DriveTrain0.3101.0000.3300.3760.2370.4110.4090.5280.2060.4540.3360.180
EngineSize0.9240.3301.0000.8080.664-0.862-0.7700.3390.2690.1660.8350.675
Horsepower0.8150.3760.8081.0000.450-0.796-0.7070.3090.2570.2260.7190.502
Length0.5660.2370.6640.4501.000-0.537-0.4350.3760.3000.2870.7100.888
MPG_City-0.8340.411-0.862-0.796-0.5371.0000.9390.2740.2290.503-0.865-0.618
MPG_Highway-0.7390.409-0.770-0.707-0.4350.9391.0000.2710.1510.543-0.816-0.538
Make0.3690.5280.3390.3090.3760.2740.2711.0000.9580.2260.3040.292
Origin0.2590.2060.2690.2570.3000.2290.1510.9581.0000.1730.2160.231
Type0.2740.4540.1660.2260.2870.5030.5430.2260.1731.0000.2790.303
Weight0.7840.3360.8350.7190.710-0.865-0.8160.3040.2160.2791.0000.785
Wheelbase0.6100.1800.6750.5020.888-0.618-0.5380.2920.2310.3030.7851.000

Missing values

2025-08-13T15:32:18.390434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-08-13T15:32:18.427109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MakeModelTypeOriginDriveTrainMSRPInvoiceEngineSizeCylindersHorsepowerMPG_CityMPG_HighwayWeightWheelbaseLength
0AcuraMDXSUVAsiaAll$36,945$33,3373.56.026517234451106189
1AcuraRSX Type S 2drSedanAsiaFront$23,820$21,7612.04.020024312778101172
2AcuraTSX 4drSedanAsiaFront$26,990$24,6472.44.020022293230105183
3AcuraTL 4drSedanAsiaFront$33,195$30,2993.26.027020283575108186
4Acura3.5 RL 4drSedanAsiaFront$43,755$39,0143.56.022518243880115197
5Acura3.5 RL w/Navigation 4drSedanAsiaFront$46,100$41,1003.56.022518243893115197
6AcuraNSX coupe 2dr manual SSportsAsiaRear$89,765$79,9783.26.029017243153100174
7AudiA4 1.8T 4drSedanEuropeFront$25,940$23,5081.84.017022313252104179
8AudiA41.8T convertible 2drSedanEuropeFront$35,940$32,5061.84.017023303638105180
9AudiA4 3.0 4drSedanEuropeFront$31,840$28,8463.06.022020283462104179
MakeModelTypeOriginDriveTrainMSRPInvoiceEngineSizeCylindersHorsepowerMPG_CityMPG_HighwayWeightWheelbaseLength
418VolvoS60 2.5 4drSedanEuropeAll$31,745$29,9162.55.020820273903107180
419VolvoS60 T5 4drSedanEuropeFront$34,845$32,9022.35.024720283766107180
420VolvoS60 R 4drSedanEuropeAll$37,560$35,3822.55.030018253571107181
421VolvoS80 2.9 4drSedanEuropeFront$37,730$35,5422.96.020820283576110190
422VolvoS80 2.5T 4drSedanEuropeAll$37,885$35,6882.55.019420273691110190
423VolvoC70 LPT convertible 2drSedanEuropeFront$40,565$38,2032.45.019721283450105186
424VolvoC70 HPT convertible 2drSedanEuropeFront$42,565$40,0832.35.024220263450105186
425VolvoS80 T6 4drSedanEuropeFront$45,210$42,5732.96.026819263653110190
426VolvoV40WagonEuropeFront$26,135$24,6411.94.017022292822101180
427VolvoXC70WagonEuropeAll$35,145$33,1122.55.020820273823109186